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Journal of Biomedicine and Biotechnology
Volume 2011, Article ID 503068, 9 pages
http://dx.doi.org/10.1155/2011/503068
Review Article

Computational Vaccinology: An Important Strategy to Discover New Potential S. mansoni Vaccine Candidates

1Departamento de Bioquímica, Imunologia do Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, 31270-901 Belo Horizonte, MG, Brazil
2Instituto Nacional de Ciência e Tecnologia em Doenças Tropicais (INCT-DT), CNPq MCT, 31270-901, BA, Brazil
3Departamento de Biologia Geral do Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, 31270-901 Belo Horizonte, MG, Brazil

Received 1 June 2011; Accepted 12 August 2011

Academic Editor: Anne S. De Groot

Copyright © 2011 Carina S. Pinheiro et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The flatworm Schistosoma mansoni is a blood fluke parasite that causes schistosomiasis, a debilitating disease that occurs throughout the developing world. Current schistosomiasis control strategies are mainly based on chemotherapy, but many researchers believe that the best long-term strategy to control schistosomiasis is through immunization with an antischistosomiasis vaccine combined with drug treatment. Several papers on Schistosoma mansoni vaccine and drug development have been published in the past few years, representing an important field of study. The advent of technologies that allow large-scale studies of genes and proteins had a remarkable impact on the screening of new and potential vaccine candidates in schistosomiasis. In this postgenomic scenario, bioinformatic technologies have emerged as important tools to mine transcriptomic, genomic, and proteomic databases. These new perspectives are leading to a new round of rational vaccine development. Herein, we discuss different strategies to identify potential S. mansoni vaccine candidates using computational vaccinology.